Learned Image Compression with Large Capacity and Low Redundancy of Latent Representation

Learned image compression has attracted a lot of attention in recent years. Currently, popular learned image compression methods usually exploit hyperprior and autoregressive models to facilitate probability estimation and reduce the redundancy of latent representation. These models ignore different image contents, and it is difficult to eliminate the spatial redundancy of image, resulting in the performance saturation. In this work, we propose a learned image compression method with large capacity and low redundancy of latent representation. We design two enhancement modules, i.e., the network capacity expansion module (NCEM) and the high-entropy content guided reconstruction module (HCGR), to construct network architectures with better rate-distortion performance than the existing hyperprior and autoregressive models. Experimental results show that our method can produce superior results compared to the state-of-the-art methods.